MiniMax M1 — reviews, specs & pricing

MiniMax's open-weight reasoning model with extremely long context.

Summary

MiniMax M1 is a 456B parameter (45.9B active) hybrid-attention MoE reasoning model supporting up to 1M tokens of context, trained with efficient large-scale reinforcement learning. It's released under Apache 2.0. It targets long-context agentic and reasoning tasks.

Sample use case

Used for long-document analysis, extended agentic workflows, and research on efficient long-context RL training. Suited for tasks needing million-token context.

Specifications

  • Provider: minimax
  • License: open
  • Parameters: 456B
  • Context: 1000k tokens
  • Input price: $0.4/M tok
  • Output price: $2.2/M tok
  • Released: 2025-06-16

Pros

  • 1M token context
  • Apache 2.0 license
  • Efficient RL training approach

Cons

  • Requires heavy compute to self-host
  • Newer, fewer third-party evaluations
  • Ecosystem still developing

Average rating 0.0 from 0 community reviews on Reviuws.

Frequently asked questions

What is MiniMax M1?

MiniMax M1 is a 456B parameter (45.9B active) hybrid-attention MoE reasoning model supporting up to 1M tokens of context, trained with efficient large-scale reinforcement learning. It's released under Apache 2.0. It targets long-context agentic and reasoning tasks.

How much does MiniMax M1 cost?

MiniMax M1 costs $0.4 per million input tokens and $2.2 per million output tokens.

Is MiniMax M1 open source?

MiniMax M1 is released under the open license.